In the past years, active noise control (ANC) gained more and more popularity on the consumer market. It suppresses environmental noise, whilst preserving the wearing comfort of headphones through compact design. Adaptive approaches are a great way to ensure good performance for varying acoustic conditions but come at the cost of high computational complexity. Although modern processor technology for embedded systems is quite powerful, even at small package sizes, it is not capable of handling more advanced algorithms such as the Kalman filter (KF), especially at high sampling rates and low latencies. Still, the advantageous properties of the KF, such as low sensitivity towards eigenvalue spread and good tracking capabilities, cannot be overseen. In this contribution, we propose a novel approach to the KF which allows for a complexity-performance-trade-off and comes with similar performance at a fraction of the complexity of the KF.